Despite different strategies for improving behavioral factors, dental caries (tooth decay) remains to be one of the most prevalent oral diseases and a challenging public health problem far from being controlled. In addition to environmental factors, recent studies have provided convincing evidence that genetics also plays an important role in the etiology of dental caries. However, to date, genetic studies on caries are still in an early stage compared to numerous efforts that have been made in other complex diseases or traits. In this proposal, to complement the traditional single marker/gene, we will develop innovative strategies to identify groups of functional related genes with enriched associations with dental caries in genome-wide association studies (GWAS) dataset. Our Specific Aims are as follows. (1) To develop a novel statistical method based on mixed effects models to identify genes and gene sets that have enriched association signals in GWAS. We will model all the genes and SNPs within a pathway in a hierarchical fashion using random gene effects, which will provide the ability to borrow information across genes in the same pathway. (2) To develop a novel dense module searching algorithm for identifying genes and gene modules (subnetworks) with enriched association signals on the human protein-protein interaction (PPI) networks. In addition to increased power, the identified subnetworks will also enable us to detect weakly associated genes playing central roles in the protein network by interconnecting many disease genes. (3) To perform an integrative analysis for ranking caries genes identified by Aims 1 and 2 and genes implicated by other genetic and genomic studies and to make all the data publicly available via a user-friendly web interface. We will apply the methods developed in Aims 1 and 2 to the GENEVA dental caries GWAS dataset (dbGap accession no: phs000095.v1.p1). We will then collect, organize and curate the genes identified, along with those from previous studies based on linkage scans, gene expression, and literature searches, and then develop multi-dimensional evidence-based approaches to prioritize these genes for future validation and follow up bioinformatics analysis. The successful completion of this project will provide us with important tools for integrative genomic analysis of current and future GWAS in caries (as well as other complex diseases), a user-friendly online system for caries research, and a list of prioritized candidate genes for future validation.